Chronic obstructive pulmonary disease (COPD) is a highly and increasingly prevalent disorder referring to a group of lung diseases that block airflow during exhalation and make it increasingly difficult to breathe. COPD can cause coughing that produces large amounts of mucus, wheezing, shortness of breath, chest tightness, and other symptoms. Emphysema and chronic asthmatic bronchitis are the two main conditions that make up COPD. Cigarette smoking is the leading cause of COPD. Most people who have COPD smoke or used to smoke. Long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust, also may contribute to COPD. In all cases, damage to lung airways may eventually interfere with the exchange of oxygen and carbon dioxide in the lungs, which can lead to serious bodily injury. COPD is identified by airway limitations that may arise from progressive emphysematous lung destruction, small airways disease or a combination of both. COPD is a heterogeneous disorder that arises from pathological processes including emphysematous lung tissue destruction, gross airway disease and functional small airway disease (fSAD) in varying combinations and severity within an individual patient. It is widely accepted that fSAD and emphysema are the two main components of COPD and that a spectrum of COPD phenotypes with varying contributions of these components exists in individual patients.
Numerous techniques have been used in attempting to measure COPD, including numerous imaging techniques. Computed tomography (CT) is a minimally invasive imaging technique that is capable of providing both high contrast and detailed resolution of the pulmonary system and that has been used to aid physicians in identifying structural abnormalities associated with COPD. Although CT is primarily used qualitatively (i.e., through visual inspection), research has been devoted to the application of quantitative CT, measured in Hounsfield Units (HU), for identifying underlying specific COPD phenotypes, with the hopes that such quantitative techniques would dictate an effective treatment strategy for the patient. Knowing the precise COPD phenotype for an individual patient, including the location, type, and severity of damage throughout the lungs would allow for the formulation of a tailored treatment regimen that accounts for the patient's specific disease state. Currently, the inability of medical professionals to accurately diagnose a patient's COPD phenotype inhibits such tailored and targeted treatment.
As indicated, a variety of CT-based metrics have been evaluated separately on inspiratory and expiratory CT scans or in combination. The most widely used technique is the lung relative volume of emphysema known as Low Attenuation Areas (LAA), which determines the sum of all image voxels with HU<−950 normalized to total inspiratory lung volume on a quantitative CT scan. This metric is easily calculated using standard imaging protocols making it readily measurable at clinical sites for evaluation. In addition, and most importantly, the LAA approach has been validated by pathology. However, this metric only identifies one extreme (i.e., emphysema) of the spectrum of underlying COPD phenotypes. Nevertheless, the validation of LAA has prompted researches to investigate the utility of inspiratory and expiratory CT scans, either analyzed individually as with LAA or in unison, to identify imaging biomarkers that provide for a more accurate correlate of COPD.
Various approaches have been evaluated for assessing COPD severity using serial CT images, which may be phasic or temporal. Previous studies have evaluated different methods for analyzing expiratory and inspiratory CT scans to provide information on air trapping in patients with COPD. The work of Matsuoka et at. (Matsuoka et al., “Quantitative Assessment of Air Trapping in Chronic Obstructive Pulmonary Disease Using Inspiratory and Expiratory Volumetric MDCT,” American Journal of Roentgenology, 190, 762-769 (2008)) has shown that exclusion of emphysematous lung from their analysis improved the correlation of their metric, the relative volume change (860-950 HU), to pulmonary function tests (PFTs). Although a strong correlation to PFTs was clearly demonstrated, no direct comparison of the relative volume change (860-950 HU) was performed to LAA since the motivation of their work was to identify air trapping. In addition, this work, as well as other work, used non-registered data sets for deriving the CT-based metrics of COPD severity. These metrics are easily derived from standard CT protocols, but only provide a global measure of COPD severity lacking the ability to interpret spatial information within the CT scans.
To make up for this deficiency, researchers are engaged in applying advanced deformable image registration algorithms between thoracic CT images. Different approaches have been applied for analyzing registered data sets as a means for assessing COPD severity or disease progression. For example, Reinhardt et al. (Reinhardt et al., “Registration-Based Estimates of Local Lung Tissue Expansion Compared to Xenon CT Measures of Specific Ventilation,” Medical Image Analysis, 12, 752-763 (2008)) have demonstrated in an animal model that the Jacobian, a measure of the specific volume change, obtained from two registered CT lung images at different phases correlates with lung function.
As a means for assessing emphysema progression, Gorbunova et al. (Gorbunova et al., “Early Detection of Emphysema Progression. Medical Image Computing and Computer-Assisted Intervention,” International Conference on Medical Image Computing and Computer-Assisted Intervention, 13, 193-200 (2010); and Gorbunova et al., “Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT,” International Conference on Medical Image Computing and Computer-Assisted Intervention, 11, 863-870 (2008)) have demonstrated two approaches for analyzing registered longitudinal inspiratory CT data. The first method relies on identifying density differences in the longitudinal inspiration level in the two scans, while the second identifies local dissimilarities between longitudinal scans. Both approaches were found to correlate with emphysema progression as determined by LAA.
Although extensive research has been devoted to evaluate CT-based techniques for assessing COPD severity, no effective techniques have been developed for using CT-based imaging to identify COPD phenotypes beyond the emphysema metric. Accordingly, a need exists for a system and method for assessing COPD status that is able to classify local variations in lung function that provides global measures as well as local measures of COPD severity. A need also exists for a robust imaging-based biomarker that allows for visualization and quantification of COPD phenotypes.